Actuary of the future 2016 Israeli Association of Actuaries Amit - - PowerPoint PPT Presentation

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Actuary of the future 2016 Israeli Association of Actuaries

Amit Parmar, Actuary Tel Aviv December 2016

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The future Changing Role of the Actuary

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Actuarial Work Changing Role of the Actuary

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Data Spec Clean Data Summarise Data Analyse Data

• Modelling
• Assumptions

Recommendations Communication

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Gap between the actuaries and:

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Policyholders Underwriters Management / CEO Rating Agency / Regulator Shareholders / Board Government

Knowledge Perception Expectation

Actuarial Work Changing Role of the Actuary

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Actuarial Work Better communication

• Explaining sensitivities
• Looking at dependencies and correlations
• Explaining effect on output

Spend more time

• Over fitting
• Over confidence bias
• Representative heuristic

Be aware of

• Mix of detail and high level
• Good use of charts and stats

Present

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Scenario Testing Stress Testing Reverse Testing

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Actuarial Work Better Communication

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Explore sensitivity of losses by return period to changes in exposure primary modifiers

– 9 Locations – Includes all modelled unique exposure combinations (more than 30K)

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Locations analysed:

• Ashdod
• Haifa
• Hevel Eilot
• Jerusalem
• Nazareth
• Netanya
• Petah Tikva
• Rishon LeZion
• Tel Aviv

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B

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A

Actuarial Work Scenario Testing– Cat modelling test C1-1

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Compare return periods of different ground motions from the models to reference views

– Global Earthquake Model Earthquake Model for the Middle East (EMME) (2016)

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Locations analysed:

• Ashdod
• Haifa
• Hevel Eilot
• Jerusalem
• Nazareth
• Netanya
• Petah Tikva
• Rishon LeZion
• Tel Aviv

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B

Actuarial Work Scenario Testing - C3-2 Hazard Intensity Return Period

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Compare frequencies of earthquakes of varying magnitudes from the models to reference views:

– Global Earthquake Model Earthquake Model for the Middle East (EMME) (2016) – Shapira et al. (2007) report from the Geophysical Institute of Israel

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Model B

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ZONE BY ZONE ANALYSIS

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Model A

Actuarial Work Scenario Testing - C3-3 Event Frequency by Severity

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Actuarial Work Reverse Stress Testing

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• Accumulation around particular point of interest
• Terror target
• Rivers
• Particular historical scenario
• Man made scenario
• Monitor exposure
• Solvency 2
• Scenario modelling

Actuarial Work Reverse Stress Testing

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Earnings Volatility Average Net Profit 1:200 Net Loss 1:100 Net Underwriting Result Probability COR < 90%

QS XL

Actuarial Work Reinsurance decision making

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Report states Iterate Feedback and control

THINGS WITH NETWORKED SENSORS DATA STORES ANALYTIC ENGINES

The future Internet of Things

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Earthquakes are predicted using the statistical analysis of historical and instrumental data. The corresponding event-to-year mapping is supplied with the model A synthetic earthquake catalogue is generated by Monte Carlo simulation on the probabilistic function used to generate events. Earthquake sources are modelled by real source zones.

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Actuarial Work Modelling Techniques

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The future Impacting Every Part of the Insurance Value Chain

• Types of sensors
• Sensor output
• Type of network
• Feedback control

effectiveness

Product Design

• New data

elements

• New pricing

algorithms based

• n

models/analyses

Pricing

• New elements in

scores and decisions: based

• n prior or

current output of sensors

• New kinds of

data and information (video or images)

Underwriting

• Responsible for

feedback and control operation

• Must work well

with people and

• bjects
• Must understand

how to impact motivation and behavior

Policyholder Service Claims

• Use new data

elements, models, analyses to understand causation and responsibility

• Fraud mitigation

tools use broader and better data and algorithms

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Deliver results in holistic way Feedback loop Scenario test Stress test Reverse stress test Learn new techniques Invest in data and technology Communicate what has been done Communicate what hasn’t been done Understand the basics What are we trying to achieve Build model fit for purpose

Summary

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Discussion and Questions

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The data and analysis provided by Guy Carpenter herein or in connection herewith are provided “as is”, without warranty of any kind whether express or implied. The analysis is based upon data provided by or obtained from external sources, the accuracy

• f which has not been independently verified by Guy Carpenter. Neither Guy Carpenter, its affiliates nor their officers, directors,

agents, modelers, or subcontractors (collectively, “Providers”) guarantee or warrant the correctness, completeness, currentness, merchantability, or fitness for a particular purpose of such data and analysis. The data and analysis is intended to be used solely for the purpose of internal evaluation and shall not disclose the analysis to any third party, except its reinsurers, auditors, rating agencies and regulators, without Guy Carpenter’s prior written consent. In the event that discloses the data and analysis

• r any portion thereof, to any permissible third party, shall adopt the data and analysis as its own. In no event will any Provider

be liable for loss of profits or any other indirect, special, incidental and/or consequential damage of any kind howsoever incurred

• r designated, arising from any use of the data and analysis provided herein or in connection herewith.

Statements or analysis concerning or incorporating tax, accounting or legal matters should be understood to be general

• bservations or applications based solely on our experience as reinsurance brokers and risk consultants and may not be relied

upon as tax, accounting or legal advice, which we are not authorized to provide. All such matters should be reviewed with the client's own qualified advisors in these areas. This presentation (report, letter) is not intended to be a complete actuarial communication. Upon request, we can prepare one. We are available to respond to questions regarding our analysis. There are many limitations on actuarial analyses, including uncertainty in the estimates and reliance on data. We will provide additional information regarding these limitations upon request. As with any actuarial analysis, the results presented herein are subject to significant variability. While these estimates represent

• ur best professional judgment, it is probable that the actual results will differ from those projected. The degree of such variability

could be substantial and could be in either direction from our estimates. The estimated cash flows may vary significantly from amounts actually collected, particularly in the event that a reinsurer is unwilling or unable to perform in accordance with the terms of the reinsurance contract. The results in this report are generated with software models provided by AIR Worldwide Corporation. Developing models to estimate losses resulting from catastrophes or other large-scale events is an inherently subjective and imprecise process, involving judgment about a variety of environmental, demographic and regulatory factors. The assumptions and methodologies used by AIR in creating the models may not constitute the exclusive set of reasonable assumptions and

• methodologies. The use of alternative assumptions and methodologies could yield materially different results. Also, the output of

the models depends on data and inputs supplied by others, and any gaps, inaccuracies, or changes to the inputs can substantially affect the output.

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GC AnalyticsTM Disclaimer(s)

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Israel C3-3 Event Frequency by Severity

Compare frequencies of earthquakes of varying magnitudes from the models to reference views:

– Global Earthquake Model Earthquake Model for the Middle East (EMME) (2016) – Shapira et al. (2007) report from the Geophysical Institute of Israel

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SUMMARIES BY ZONE BY MAGNITUDE

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